{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import jieba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Theme</th>\n",
       "      <th>URL</th>\n",
       "      <th>Content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>汽车</td>\n",
       "      <td>新辉腾　４．２　Ｖ８　４座加长Ｉｎｄｉｖｉｄｕａｌ版２０１１款　最新报价</td>\n",
       "      <td>http://auto.data.people.com.cn/model_15782/</td>\n",
       "      <td>经销商　电话　试驾／订车Ｕ憬杭州滨江区江陵路１７８０号４００８－１１２２３３转５８６４＃保常...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>汽车</td>\n",
       "      <td>９１８　Ｓｐｙｄｅｒ概念车</td>\n",
       "      <td>http://auto.data.people.com.cn/prdview_165423....</td>\n",
       "      <td>呼叫热线　４００８－１００－３００　服务邮箱　ｋｆ＠ｐｅｏｐｌｅｄａｉｌｙ．ｃｏｍ．ｃｎ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>汽车</td>\n",
       "      <td>日内瓦亮相　ＭＩＮＩ性能版／概念车－１．６Ｔ引擎</td>\n",
       "      <td>http://auto.data.people.com.cn/news/story_5249...</td>\n",
       "      <td>ＭＩＮＩ品牌在二月曾经公布了最新的ＭＩＮＩ新概念车Ｃｌｕｂｖａｎ效果图，不过现在在日内瓦车展...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>汽车</td>\n",
       "      <td>清仓大甩卖一汽夏利Ｎ５威志Ｖ２低至３．３９万</td>\n",
       "      <td>http://auto.data.people.com.cn/news/story_6144...</td>\n",
       "      <td>清仓大甩卖！一汽夏利Ｎ５、威志Ｖ２低至３．３９万＝日，启新中国一汽强势推出一汽夏利Ｎ５、威志...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>汽车</td>\n",
       "      <td>大众敞篷家族新成员　高尔夫敞篷版实拍</td>\n",
       "      <td>http://auto.data.people.com.cn/news/story_5686...</td>\n",
       "      <td>在今年３月的日内瓦车展上，我们见到了高尔夫家族的新成员，高尔夫敞篷版，这款全新敞篷车受到了众...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Category                                 Theme  \\\n",
       "0       汽车  新辉腾　４．２　Ｖ８　４座加长Ｉｎｄｉｖｉｄｕａｌ版２０１１款　最新报价   \n",
       "1       汽车                         ９１８　Ｓｐｙｄｅｒ概念车   \n",
       "2       汽车              日内瓦亮相　ＭＩＮＩ性能版／概念车－１．６Ｔ引擎   \n",
       "3       汽车                清仓大甩卖一汽夏利Ｎ５威志Ｖ２低至３．３９万   \n",
       "4       汽车                    大众敞篷家族新成员　高尔夫敞篷版实拍   \n",
       "\n",
       "                                                 URL  \\\n",
       "0        http://auto.data.people.com.cn/model_15782/   \n",
       "1  http://auto.data.people.com.cn/prdview_165423....   \n",
       "2  http://auto.data.people.com.cn/news/story_5249...   \n",
       "3  http://auto.data.people.com.cn/news/story_6144...   \n",
       "4  http://auto.data.people.com.cn/news/story_5686...   \n",
       "\n",
       "                                             Content  \n",
       "0  经销商　电话　试驾／订车Ｕ憬杭州滨江区江陵路１７８０号４００８－１１２２３３转５８６４＃保常...  \n",
       "1       呼叫热线　４００８－１００－３００　服务邮箱　ｋｆ＠ｐｅｏｐｌｅｄａｉｌｙ．ｃｏｍ．ｃｎ  \n",
       "2  ＭＩＮＩ品牌在二月曾经公布了最新的ＭＩＮＩ新概念车Ｃｌｕｂｖａｎ效果图，不过现在在日内瓦车展...  \n",
       "3  清仓大甩卖！一汽夏利Ｎ５、威志Ｖ２低至３．３９万＝日，启新中国一汽强势推出一汽夏利Ｎ５、威志...  \n",
       "4  在今年３月的日内瓦车展上，我们见到了高尔夫家族的新成员，高尔夫敞篷版，这款全新敞篷车受到了众...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_news = pd.read_csv('D:\\\\Py_dataset\\\\val.txt',names = ['Category','Theme','URL','Content'],sep = '\\t',encoding = 'utf-8')\n",
    "df_news.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5000 entries, 0 to 4999\n",
      "Data columns (total 4 columns):\n",
      "Category    5000 non-null object\n",
      "Theme       5000 non-null object\n",
      "URL         5000 non-null object\n",
      "Content     5000 non-null object\n",
      "dtypes: object(4)\n",
      "memory usage: 156.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df_news.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.分词，使用Jieba分词器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'阿里巴巴集团昨日宣布，将在集团管理层面设立首席数据官岗位（Ｃｈｉｅｆ\\u3000Ｄａｔａ\\u3000Ｏｆｆｉｃｅｒ），阿里巴巴Ｂ２Ｂ公司ＣＥＯ陆兆禧将会出任上述职务，向集团ＣＥＯ马云直接汇报。＞菹ぃ和６月初的首席风险官职务任命相同，首席数据官亦为阿里巴巴集团在完成与雅虎股权谈判，推进“ｏｎｅ\\u3000ｃｏｍｐａｎｙ”目标后，在集团决策层面新增的管理岗位。０⒗锛团昨日表示，“变成一家真正意义上的数据公司”已是战略共识。记者刘夏'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content = df_news['Content'].values.tolist()\n",
    "content[1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from the default dictionary ...\n",
      "Loading model from cache C:\\Users\\taon1\\AppData\\Local\\Temp\\jieba.cache\n",
      "Loading model cost 0.786 seconds.\n",
      "Prefix dict has been built succesfully.\n"
     ]
    }
   ],
   "source": [
    "content_S = []\n",
    "\n",
    "for line in content:\n",
    "    current_segment = jieba.lcut(line)\n",
    "    if len(current_segment) > 1 and current_segment != '\\r\\n':\n",
    "        content_S.append(current_segment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['阿里巴巴', '集团', '昨日', '宣布', '，', '将', '在', '集团', '管理', '层面', '设立', '首席', '数据', '官', '岗位', '（', 'Ｃ', 'ｈ', 'ｉ', 'ｅ', 'ｆ', '\\u3000', 'Ｄ', 'ａ', 'ｔ', 'ａ', '\\u3000', 'Ｏ', 'ｆ', 'ｆ', 'ｉ', 'ｃ', 'ｅ', 'ｒ', '）', '，', '阿里巴巴', 'Ｂ', '２', 'Ｂ', '公司', 'Ｃ', 'Ｅ', 'Ｏ', '陆兆禧', '将', '会', '出任', '上述', '职务', '，', '向', '集团', 'Ｃ', 'Ｅ', 'Ｏ', '马云', '直接', '汇报', '。', '＞', '菹', 'ぃ', '和', '６', '月初', '的', '首席', '风险', '官', '职务', '任命', '相同', '，', '首席', '数据', '官亦为', '阿里巴巴', '集团', '在', '完成', '与', '雅虎', '股权', '谈判', '，', '推进', '“', 'ｏ', 'ｎ', 'ｅ', '\\u3000', 'ｃ', 'ｏ', 'ｍ', 'ｐ', 'ａ', 'ｎ', 'ｙ', '”', '目标', '后', '，', '在', '集团', '决策', '层面', '新增', '的', '管理', '岗位', '。', '０', '⒗', '锛', '团', '昨日', '表示', '，', '“', '变成', '一家', '真正', '意义', '上', '的', '数据', '公司', '”', '已', '是', '战略', '共识', '。', '记者', '刘夏']\n"
     ]
    }
   ],
   "source": [
    "print(content_S[1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Content_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[经销商, 　, 电话, 　, 试驾, ／, 订车, Ｕ, 憬, 杭州, 滨江区, 江陵, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[呼叫, 热线, 　, ４, ０, ０, ８, －, １, ０, ０, －, ３, ０, ０...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[Ｍ, Ｉ, Ｎ, Ｉ, 品牌, 在, 二月, 曾经, 公布, 了, 最新, 的, Ｍ, Ｉ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[清仓, 大, 甩卖, ！, 一汽, 夏利, Ｎ, ５, 、, 威志, Ｖ, ２, 低至, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[在, 今年, ３, 月, 的, 日内瓦, 车展, 上, ，, 我们, 见到, 了, 高尔夫...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           Content_S\n",
       "0  [经销商, 　, 电话, 　, 试驾, ／, 订车, Ｕ, 憬, 杭州, 滨江区, 江陵, ...\n",
       "1  [呼叫, 热线, 　, ４, ０, ０, ８, －, １, ０, ０, －, ３, ０, ０...\n",
       "2  [Ｍ, Ｉ, Ｎ, Ｉ, 品牌, 在, 二月, 曾经, 公布, 了, 最新, 的, Ｍ, Ｉ...\n",
       "3  [清仓, 大, 甩卖, ！, 一汽, 夏利, Ｎ, ５, 、, 威志, Ｖ, ２, 低至, ...\n",
       "4  [在, 今年, ３, 月, 的, 日内瓦, 车展, 上, ，, 我们, 见到, 了, 高尔夫..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_content = pd.DataFrame({'Content_S':content_S})\n",
    "df_content.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stopwords</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>$</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  stopwords\n",
       "0         !\n",
       "1         \"\n",
       "2         #\n",
       "3         $\n",
       "4         %"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stopwords = pd.read_csv('D:\\\\Py_dataset\\\\stopwords.txt',index_col = False,names = ['stopwords'],sep = '\\t',quoting = 3,encoding = 'utf-8')\n",
    "stopwords.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#去停用词\n",
    "\n",
    "def drop_stopwords(contents,stopwords):\n",
    "    contents_clean = []\n",
    "    all_words = []\n",
    "    for line in contents:\n",
    "        line_clean = []\n",
    "        for word in line:\n",
    "            if word in stopwords:\n",
    "                continue\n",
    "            line_clean.append(word)\n",
    "            all_words.append(str(word))\n",
    "        contents_clean.append(line_clean)\n",
    "    return contents_clean,all_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "Contents = df_content['Content_S'].values.tolist()\n",
    "Stopwords = stopwords['stopwords'].values.tolist()\n",
    "\n",
    "contents_clean,all_words = drop_stopwords(Contents,Stopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>contents_clean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[经销商, 电话, 试驾, 订车, Ｕ, 憬, 杭州, 滨江区, 江陵, 路, 号, 转, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[呼叫, 热线, 服务, 邮箱, ｋ, ｆ, ｐ, ｅ, ｏ, ｐ, ｌ, ｅ, ｄ, ａ,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[Ｍ, Ｉ, Ｎ, Ｉ, 品牌, 二月, 公布, 最新, Ｍ, Ｉ, Ｎ, Ｉ, 新, 概念...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[清仓, 甩卖, 一汽, 夏利, Ｎ, 威志, Ｖ, 低至, 万, 启新, 中国, 一汽, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[日内瓦, 车展, 见到, 高尔夫, 家族, 新, 成员, 高尔夫, 敞篷版, 款, 全新,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      contents_clean\n",
       "0  [经销商, 电话, 试驾, 订车, Ｕ, 憬, 杭州, 滨江区, 江陵, 路, 号, 转, ...\n",
       "1  [呼叫, 热线, 服务, 邮箱, ｋ, ｆ, ｐ, ｅ, ｏ, ｐ, ｌ, ｅ, ｄ, ａ,...\n",
       "2  [Ｍ, Ｉ, Ｎ, Ｉ, 品牌, 二月, 公布, 最新, Ｍ, Ｉ, Ｎ, Ｉ, 新, 概念...\n",
       "3  [清仓, 甩卖, 一汽, 夏利, Ｎ, 威志, Ｖ, 低至, 万, 启新, 中国, 一汽, ...\n",
       "4  [日内瓦, 车展, 见到, 高尔夫, 家族, 新, 成员, 高尔夫, 敞篷版, 款, 全新,..."
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_contents = pd.DataFrame({'contents_clean':contents_clean})\n",
    "df_contents.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>all_words</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>经销商</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>电话</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>试驾</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>订车</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ｕ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  all_words\n",
       "0       经销商\n",
       "1        电话\n",
       "2        试驾\n",
       "3        订车\n",
       "4         Ｕ"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all_words = pd.DataFrame({'all_words':all_words})\n",
    "df_all_words.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "中     5199\n",
       "中国    3115\n",
       "说     3055\n",
       "Ｓ     2646\n",
       "万     2390\n",
       "Name: all_words, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words_count = df_all_words['all_words'].value_counts()\n",
    "words_count[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "words_df = pd.DataFrame(words_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "words_df = words_df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "words_df.rename(columns = {'index':'words','all_words':'count'},inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>words</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>中</td>\n",
       "      <td>5199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>中国</td>\n",
       "      <td>3115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>说</td>\n",
       "      <td>3055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ｓ</td>\n",
       "      <td>2646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>万</td>\n",
       "      <td>2390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ｔ</td>\n",
       "      <td>2310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ａ</td>\n",
       "      <td>2306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ｉ</td>\n",
       "      <td>2287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ｅ</td>\n",
       "      <td>2270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ｏ</td>\n",
       "      <td>2236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Ｐ</td>\n",
       "      <td>2191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Ｃ</td>\n",
       "      <td>2051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Ｍ</td>\n",
       "      <td>2036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Ｖ</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Ｉ</td>\n",
       "      <td>1772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>市场</td>\n",
       "      <td>1762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ｎ</td>\n",
       "      <td>1743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ｄ</td>\n",
       "      <td>1721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Ｌ</td>\n",
       "      <td>1696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Ｎ</td>\n",
       "      <td>1655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   words  count\n",
       "0      中   5199\n",
       "1     中国   3115\n",
       "2      说   3055\n",
       "3      Ｓ   2646\n",
       "4      万   2390\n",
       "5      Ｔ   2310\n",
       "6      ａ   2306\n",
       "7      ｉ   2287\n",
       "8      ｅ   2270\n",
       "9      ｏ   2236\n",
       "10     Ｐ   2191\n",
       "11     Ｃ   2051\n",
       "12     Ｍ   2036\n",
       "13     Ｖ   2006\n",
       "14     Ｉ   1772\n",
       "15    市场   1762\n",
       "16     ｎ   1743\n",
       "17     Ｄ   1721\n",
       "18     Ｌ   1696\n",
       "19     Ｎ   1655"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words_df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x21399b08978>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from wordcloud import WordCloud\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib\n",
    "\n",
    "fig = plt.figure(figsize = (10,5))\n",
    "\n",
    "wordcloud = WordCloud(font_path = 'D:\\\\Py_dataset\\\\simhei.ttf',background_color = 'white',max_font_size = 80)\n",
    "word_frequency = {x[0]:x[1] for x in words_df.head(100).values}\n",
    "wordcloud = wordcloud.fit_words(word_frequency) \n",
    "plt.imshow(wordcloud)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.TF-IDF 提取关键系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "法国ＶＳ西班牙、里贝里ＶＳ哈维，北京时间６月２４日凌晨一场的大战举世瞩目，而这场胜利不仅仅关乎两支顶级强队的命运，同时也是他们背后的球衣赞助商耐克和阿迪达斯之间的一次角逐。Ｔ谌胙”窘炫分薇的１６支球队之中，阿迪达斯和耐克的势力范围也是几乎旗鼓相当：其中有５家球衣由耐克提供，而阿迪达斯则赞助了６家，此外茵宝有３家，而剩下的两家则由彪马赞助。而当比赛进行到现在，率先挺进四强的两支球队分别被耐克支持的葡萄牙和阿迪达斯支持的德国占据，而由于最后一场１／４决赛是茵宝（英格兰）和彪马（意大利）的对决，这也意味着明天凌晨西班牙同法国这场阿迪达斯和耐克在１／４决赛的唯一一次直接交手将直接决定两家体育巨头在此次欧洲杯上的胜负。８据评估，在２０１２年足球商品的销售额能总共超过４０亿欧元，而单单是不足一个月的欧洲杯就有高达５亿的销售额，也就是说在欧洲杯期间将有７００万件球衣被抢购一空。根据市场评估，两大巨头阿迪达斯和耐克的市场占有率也是并驾齐驱，其中前者占据３８％，而后者占据３６％。体育权利顾问奥利弗－米歇尔在接受《队报》采访时说：“欧洲杯是耐克通过法国翻身的一个绝佳机会！”Ｃ仔尔接着谈到两大赞助商的经营策略：“竞技体育的成功会燃起球衣购买的热情，不过即便是水平相当，不同国家之间的欧洲杯效应却存在不同。在德国就很出色，大约１／４的德国人通过电视观看了比赛，而在西班牙效果则差很多，由于民族主义高涨的加泰罗尼亚地区只关注巴萨和巴萨的球衣，他们对西班牙国家队根本没什么兴趣。”因此尽管西班牙接连拿下欧洲杯和世界杯，但是阿迪达斯只为西班牙足协支付每年２６００万的赞助费＃相比之下尽管最近两届大赛表现糟糕法国足协将从耐克手中每年可以得到４０００万欧元。米歇尔解释道：“法国创纪录的４０００万欧元赞助费得益于阿迪达斯和耐克竞逐未来１５年欧洲市场的竞争。耐克需要笼络一个大国来打赢这场欧洲大陆的战争，而尽管德国拿到的赞助费并不太高，但是他们却显然牢牢掌握在民族品牌阿迪达斯手中。从长期投资来看，耐克给法国的赞助并不算过高。”\n",
      "耐克 阿迪达斯 欧洲杯 球衣 西班牙\n"
     ]
    }
   ],
   "source": [
    "import jieba.analyse\n",
    "index = 2400\n",
    "\n",
    "print(df_news['Content'][index])\n",
    "content_s_str = ''.join(content_S[index])\n",
    "print(' '.join(jieba.analyse.extract_tags(content_s_str,topK = 5,withWeight = False)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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